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Transcriptome-based molecular subgroup identification and prognosis stratification in pediatric AML.

YuWei Huang1, MuYao Yang2, JiaQi Hu3,4,5,6

  • 1Department of Neonatology, Shanghai Children's Medical Center GuiZhou Hospital, Shanghai Jiao Tong University School of Medicine, Guiyang, 550081, Guizhou, China.

Annals of Hematology
|October 2, 2025
PubMed
Summary

Researchers identified eight new molecular subgroups for acute myeloid leukemia (AML) using transcriptome data. A novel prognostic model was developed to improve risk stratification for AML patients.

Keywords:
Acute myeloid leukemiaMachine learningMolecular subgroupPrognostic predictionRNA-seq

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Current acute myeloid leukemia (AML) risk stratification relies on molecular and cytogenetic markers, but significant heterogeneity exists within subgroups.
  • Existing methods lack the granularity to fully capture the biological diversity and prognostic differences in pediatric AML.

Purpose of the Study:

  • To identify novel transcriptome-based molecular subgroups in AML.
  • To develop and validate a new prognostic prediction model for AML using machine learning algorithms.

Main Methods:

  • Comprehensive analysis of public AML transcriptome data from GDC Data Portal and MsigDB.
  • Clustering analysis to reclassify AML into 8 molecular subgroups.
  • Development of a prognostic model using Random Forest, SVM, XGBoost, and Decision Tree algorithms.

Main Results:

  • Eight distinct transcriptome-based molecular subgroups of AML were identified.
  • The XGBoost model demonstrated superior performance, highlighting HSD17B10, NDUFS8, ASCL5, FADS2, and COX8A as critical prognostic factors.
  • A 62-gene prognostic model showed significant predictive value in retrospective validation.

Conclusions:

  • A new molecular subpopulation of AML was identified, enhancing disease risk stratification.
  • A robust prognostic model for AML was established, offering improved predictive capabilities.
  • Further prospective analysis is warranted to confirm the model's clinical utility.